Generating a Venn diagram using a columnar database management system

ABSTRACT

Venn diagrams are computed for a given plurality of input sets. The process of computing the Venn diagrams is executed on columnar database systems for efficient execution. The computation of various subsets of the Venn diagrams is performed by determining subsets of various combinations of the input sets and computing set differences of the intersection sets. The process orders the execution of various steps of computing the subsets for the Venn diagram in an order that reduces the number of times an input set is loaded. Information describing various subsets of a Venn diagram is used to render the Venn diagram for display, for example, on a client device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims a benefit of priorityunder 35 U.S.C. § 120 from, U.S. patent application Ser. No. 15/595,448,filed May 15, 2017, entitled “GENERATING A VENN DIAGRAM USING A COLUMNARDATABASE MANAGEMENT SYSTEM,” which is a continuation of, and claims abenefit of priority under 35 U.S.C. § 120 from, U.S. patent applicationSer. No. 14/308,971, filed Jun. 19, 2014, issued as U.S. Pat. No.9,679,000, entitled “GENERATING A VENN DIAGRAM USING A COLUMNAR DATABASEMANAGEMENT SYSTEM,” which claims a benefit of priority from U.S.Provisional Application No. 61/837,272, filed Jun. 20, 2013. Allapplications listed in the paragraph are incorporated herein byreference in their entireties.

TECHNICAL FIELD

This invention relates generally to data mining, and particularly togenerating a Venn diagram using a columnar database management system.

DESCRIPTION OF THE RELATED ART

Data mining algorithms are often employed by various systems thatprocess data. Data is often represented as sets of various types ofentities, for example, products, employees, users of a system,transactions performed by an online system and so on. Data miningsystems perform operations on these sets of data, for example, setunion, set intersection, set difference, and so on. One operationperformed by data mining systems is generation of a Venn diagram of twoor more sets of data.

Generating a Venn diagram in turn requires performing various other setoperations, for example, set intersection, set difference and so on.Conventional techniques for generating Venn diagrams perform theseoperations inefficiently. This is so because conventional techniquesload the same data multiple times for performing various steps of theVenn diagram generation. As a consequence, generating a Venn diagram isoften inefficient and consumes more computing resources than needed.

SUMMARY

Embodiments of the invention generate Venn diagrams. A Venn diagramshows information describing subsets of data of two or more input sets.A data mining system receives a request for determining subsets of theVenn diagram. The request identifies the plurality of input sets for theVenn diagram. The data mining system generates intersection sets, eachintersection set based on a combination of input sets. The data miningsystem determines the intersection sets in an order that efficientlyutilizes data of input sets that has been previously loaded.

The data mining system loads a first combination of input sets fordetermining a first intersection set. The data mining system selects asecond intersection set for processing next such that the combination ofinput sets for the second intersection set includes the firstcombination of input sets. For example, the second combination can be asuperset of the first combination of input sets. The data mining systemloads the input sets of the second combination that are not included inthe first combination. The data mining system uses the loaded input setsto determine the second intersection set. The data mining systemdetermines the subsets for the Venn diagram based on the intersectionsets.

In an embodiment, the data mining system builds a truth tablerepresenting combinations of input sets as binary values. The positionsof bits in a binary value from the truth table correspond to input setsand the value of each bit determines whether the input set correspondingto that position is included in the combination. The data mining systemuses the truth table to determine the order in which steps forgenerating the Venn diagram are executed. For example, the data miningsystem ranks the combinations of sets based on the number of ones ineach binary value of the truth table and uses the ranking to select thesecond intersection set after the first intersection set is determined.

The features and advantages described in this summary and the followingdetailed description are not all-inclusive. Many additional features andadvantages will be apparent to one of ordinary skill in the art in viewof the drawings, specification, and claims hereof.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A is a high-level diagram illustrating a system environment forgenerating a Venn diagram using a columnar database management system,according to one embodiment

FIG. 1B is a diagram showing a Venn diagram.

FIG. 2 is a high-level block diagram of a computer system for datamining, according to one embodiment.

FIG. 3 is a flow chart illustrating a method for generating a Venndiagram by a columnar database management system, according to oneembodiment.

FIGS. 4A-4J illustrate an example process for generating a Venn diagramby a columnar database management system, according to one embodiment.

DETAILED DESCRIPTION

The Figures (FIGS.) and the following description describe certainembodiments by way of illustration only. One skilled in the art willreadily recognize from the following description that alternativeembodiments of the structures and methods illustrated herein may beemployed without departing from the principles described herein. It isnoted that wherever practicable similar or like reference numbers may beused in the figures and may indicate similar or like functionality.

FIG. 1A illustrates an example of a system environment 100 forgenerating a Venn diagram based on two or more input sets of data. Asshown in FIG. 1A, a client 105 is in communication with a data miningsystem 108 over a network 102. The client 105 is a computing deviceusable by a user to initiate data mining requests. The data miningrequests may indicate to the data mining system 108 to perform one ormore data mining algorithms on data stored by the data mining system108. In one embodiment, the data mining request indicates that a Venndiagram be generated based on two or more input sets of data specifiedby the data mining request. In one aspect, the client 105 may be acomputing device, such as a desktop computer, a laptop computer, aworkstation, a server computer, a mobile phone, a tablet device, etc.

As shown in FIG. 1A, the data mining system 108 includes, among othercomponents, an application frontend 110 and a columnar databasemanagement system 112. The application frontend 110 may be a softwareapplication configured to receive data mining requests from the client105 and/or provide data mining results to the client device 105. In oneembodiment, the application frontend 110 may forward or route datamining requests to the columnar database management system 112 for dataprocessing. The application frontend 110 may additionally receive datamining results from the columnar database management system 112 fortransmission to a client 105.

The columnar database management system 112 is a system configured tostore data in column oriented fashion in contrast with databases thatstore data in row oriented fashion. The columnar database managementsystem 112 stores data in column oriented fashion so as to performcomputations of aggregates over columns of data efficiently compared todatabases that store data in row oriented fashion. For example, if datais stored in row oriented fashion, loading a column of data requiresloading of data belonging to other columns as well. However, thecolumnar database management system 112 stores in column orientedfashion and loads data of a column for processing without loading databelonging to other columns. As a result the columnar database managementsystem 112 performs processing of a column of data more efficiently thandatabases that represent data in row oriented fashion. As shown in FIG.1A, the columnar database management system 112 includes data 114, datamining algorithms 115, and a database engine 116. In one aspect, data114 includes various columns of data, where each column represents aseparate logical set.

The database engine 116 is a logical entity configured to create, read,update and delete data stored by the columnar data management system112. In one embodiment, the database engine 116 is configured to performdata mining using the data mining algorithms 115 and the data 114. Inone embodiment, the data mining algorithms 115 include a Venn diagramprocess for generating Venn diagrams. Generating a Venn diagramcomprises calculation of various subsets of data displayed by a Venndiagram.

In the embodiment, responsive to receiving a data mining request togenerate a Venn diagram from the client 105 or forwarded by theapplication frontend 110, the data mining system 108 performs varioussteps to process the request and generate a Venn diagram. In particular,the data mining system 108 generates a truth table responsive to arequest to generate a Venn diagram. Furthermore, the data mining system108 performs various arithmetic and logical operations using the truthtable to generate the Venn diagram. Specifically, the database engine116 performs calculations for those sets of data involved in the Venndiagram without performing additional operations, such as negation,union, or exclusion when the operations are not needed.

The interactions between the client devices 105 and the data miningsystem 108 are typically performed via a network 102, for example, viathe internet. The network 102 enables communications between the clientdevice 105 and the data mining system 108. In one embodiment, thenetwork 108 uses standard communications technologies and/or protocols.The data exchanged over the network 108 can be represented usingtechnologies and/or formats including the hypertext markup language(HTML), the extensible markup language (XML), etc. In anotherembodiment, the entities can use custom and/or dedicated datacommunications technologies instead of, or in addition to, the onesdescribed above.

FIG. 1B is a diagram showing a Venn diagram. FIG. 1B and the otherfigures use like reference numerals to identify like elements. A letterafter a reference numeral, such as “130 a,” indicates that the textrefers specifically to the element having that particular referencenumeral. A reference numeral in the text without a following letter,such as “130,” refers to any or all of the elements in the figuresbearing that reference numeral (e.g. “130” in the text refers toreference numerals “130 a” and/or “130 b” in the figures).

The Venn diagram shows various relations between sets. The relations maybe shown pictorially as overlapping geometric shapes, for example,overlapping circles, overlapping rectangles, or other types of shapes.For example, as shown in FIG. 1B, the data mining system 108 receivesinformation identifying (i.e., specifying or defining) input sets 130 a,130 b, and 130 c (corresponding to sets A, B, and C respectively). Forexample, the input sets 130 a, 130 b, and 130 c may be stored as columnsin the columnar database management system 112, and the requestidentifies which columns store the input sets and what conditions theyhave to meet. The data mining system 108 determines subsets 120 of thesesets corresponding to the various regions shown in FIG. 1B.

For example, region 120 a corresponds to all elements of set 130 a thatdo not overlap with any other set. Region 120 b corresponds to allelements of set 130 b that do not overlap with any other set and region120 c corresponds to all elements of set 130 c that do not overlap withany other set. Region 120 g corresponds to intersection of all threesets 130 a, 130 b, and 130 c. Region 120 d corresponds to intersectionof sets 130 a and 130 b, minus the elements of region 120 g. Region 120e corresponds to intersection of sets 130 a and 130 c, minus theelements of region 120 g. Region 120 f corresponds to intersection ofsets 130 b and 130 c, minus the elements of region 120 g.

Given the various subsets corresponding to a Venn diagram, the datamining system 108 can determine other relations between the sets. Forexample, the union of 120 d and 120 g can be determined to compute theintersection of sets 130 a and 130 b. Similarly, the union of 120 e and120 g can be determined to compute the intersection of sets 130 a and130 c. Similarly, the union of 120 f and 120 g can be determined tocompute the intersection of sets 130 b and 130 c.

Computer Architecture

FIG. 2 is a high-level block diagram of a computer 200 for use as theclient 105, data mining system 108, application frontend 110, orcolumnar database management system 112 according to one embodiment.Illustrated are at least one processor 202 coupled to a chipset 204.Also coupled to the chipset 204 are a memory 206, a storage device 208,a keyboard 210, a graphics adapter 212, a pointing device 214, and anetwork adapter 216. A display 218 is coupled to the graphics adapter212. In one embodiment, the functionality of the chipset 204 is providedby a memory controller hub 220 and an I/O controller hub 222. In anotherembodiment, the memory 206 is coupled directly to the processor 202instead of to the chipset 204.

The storage device 208 is a non-transitory computer-readable storagemedium, such as a hard drive, compact disk read-only memory (CD-ROM),DVD, or a solid-state memory device. In an embodiment, instructions forperforming various steps of the process of generating Venn diagram arestored on a storage device 208. The memory 206 holds instructions anddata used by the processor 202. The pointing device 214 may be a mouse,track ball, or other type of pointing device, and is used in combinationwith the keyboard 210 to input data into the computer system 200. Thegraphics adapter 212 displays images and other information on thedisplay 218. The network adapter 216 couples the computer system 200 tothe network 102.

A computer 200 can have different and/or other components than thoseshown in FIG. 2. In addition, the computer 200 can lack certainillustrated components. In one embodiment, a computer 200 acting as thedata mining system 108 is formed of multiple blade computers and lacks akeyboard 210, pointing device 214, graphics adapter 212, and/or display218. Moreover, the storage device 208 can be local and/or remote fromthe computer 200 (such as embodied within a storage area network (SAN)).

The computer 200 is adapted to execute computer program modules forproviding functionality described herein. As used herein, the term“module” refers to computer program logic utilized to provide thespecified functionality. Thus, a module can be implemented in hardware,firmware, and/or software. In one embodiment, program modules are storedon the storage device 208, loaded into the memory 206, and executed bythe processor 202.

Embodiments of the entities described herein can include other and/ordifferent modules than the ones described here. In addition, thefunctionality attributed to the modules can be performed by other ordifferent modules in other embodiments. Moreover, this descriptionoccasionally omits the term “module” for purposes of clarity andconvenience.

Overall Process

The process of generating Venn diagrams using columnar databases ordersthe various steps of the computation such that data loaded forperforming a step is reused by subsequent steps if possible. The datamining system 108 receives information identifying a plurality of inputsets of data for generating a Venn diagram. In an embodiment, thecolumnar database management system 112 stores the input sets in acolumnar format that stores data of a column on a storage device. Thedata mining system 108 determines intersections of various combinationsof the input sets. For example, if the input data sets are 130 a, 130 b,and 130 c, the various combinations of intersections are (130 a ∩ 130b), (130 b ∩ 130 c), (130 a ∩ 130 c), and (130 a∩ 130 b ∩ 130 c).

The data mining system 108 orders the computations of the intersectionsof sets such that the data used for determining an intersection of acombination is used for determining the intersection of the nextcombination. For example, if data for set 130 a is loaded, theintersection (130 a ∩ 130 b) may be determined next since thisintersection uses the data of set 130 a that is already loaded. Once theintersection of data set (130 a ∩ 130 b) is determined, the data miningsystem 108 may determine the intersection of (130 a ∩ 130 b ∩ 130 c)since performing this operation requires intersection set (130 a ∩ 130b) that is already loaded.

The data mining system 108 determines the various subsets of the Venndiagram by computing appropriate set differences of the intersectionsets or subsets of Venn diagram previously computed. For example, thesubset 120 d of the Venn diagram is determined by computing the setdifference of intersection set (130 a ∩ 130 b) and the intersection set(130 a ∩ 130 b ∩ 130 c). Similarly, the subset 120 e of the Venn diagramis determined by computing the set difference of intersection set (130 a∩ 130 c) and the intersection set (130 a ∩ 130 b ∩ 130 c). Similarly,the subset 120 f of the Venn diagram is determined by computing the setdifference of intersection set (130 b ∩ 130 c) and the intersection set(130 a ∩ 130 b ∩ 130 c).

Furthermore, subset 120 a is computed by computing the set difference ofset 130 a and a union of subsets 120 d, 120 e, and 120 g. Subset 120 bis computed by computing the set difference of set 130 b and a union ofsubsets 120 d, 120 f, and 120 g. Subset 120 c is computed by computingthe set difference of set 130 c and a union of subsets 120 e, 120 f, and120 g.

FIG. 3 is a flowchart illustrating a method for generating a Venndiagram according to one embodiment. Other embodiments can perform thesteps of the method in different orders and can include different,additional and/or fewer steps. The method shown in FIG. 3 can beperformed by the columnar database management system 112.

In the one embodiment, the columnar database management system 112directly receives 305 a data mining request from the client 105 orreceives a forwarded data mining request from the application frontend110. The data mining request received 305 requests generation of a Venndiagram. The data mining request identifies the input sets of data forthe Venn diagram. For example, the data mining request may identify thesets of data A, B, and C. For illustration purposes, the followingdescription assumes that the set of data A includes 8 elements, the setof data B includes 10 elements, and the set of data C includes 8elements. FIG. 4A shows the elements included in the example sets ofdata A, B, and C.

In one aspect, the data mining request can be encapsulated in a suitablemessage format. For example the data mining request may be encapsulatedin a suitable text format, such as XML, or in any other format. In someembodiments, the data mining request is received by the columnardatabase management system 112 via one or more suitable network transferprotocols. In other embodiments, the data mining request is received viaan application programming interface (API) call, through receipt of afile containing the data mining request, or via an interactive console.It will be appreciated, however, that other ways of receiving the datamining request may be used.

After receiving the data mining request, the database engine 116generates 315 a truth table based on the sets of data indicated by thedata mining request. In one embodiment, the generated truth tableincludes multiple entries, where each entry corresponds to a combinationof the input sets of data. Each entry of the truth table is associatedwith a binary value. The positions of bits in a binary value correspondto input sets and the value of a bit determines whether the input setcorresponding to the position of the bit is included in the combinationrepresented by the binary value.

For example, counting from the least significant bit to the mostsignificant bit, the first position of bits may correspond to set C, thesecond position may correspond to set B, and the third position maycorrespond to set A. Furthermore, if a bit value is 1, the correspondingset is included in the combination and if the bit value is 0, thecorresponding set is not included in the combination. A value 1 in thebinary representation corresponds to value “true” and value 0corresponds to value “false.” However, a different encoding scheme canbe used, for example, an encoding scheme in which value 1 in the binaryrepresentation corresponds to value “false” and value 0 corresponds tovalue “true.”

The data mining system 108 ranks the combinations of sets based on thenumber of ones in each binary value. This ranking is distinct from aranking based on the numeric value of the binary number. For example,even though binary number 1000 is greater that binary number 011, thenumber of ones in 011 is more than the number of ones in 1000.Accordingly, in the ranking based on the number of ones, 011 is rankedafter the value 1000. The data mining system 108 uses this ranking toselect the next intersection set to process after a particularintersection set is processed.

FIG. 4B shows an example of a truth table 402 generated by the databaseengine 116 for the input sets of data A, B, and C. As shown in FIG. 4B,the truth table 402 includes 8 separate entries 404 for the variouscombinations of the sets of data A, B, and C. For example, the entry forthe combination 6 corresponds to an intersection of the sets of data Aand B. This is so because the binary representation of value 6 is 110.The second position corresponding to set B and the third positioncorresponding to set A are both 1 indicating that sets B and A areincluded in this combination. Furthermore, the first bit position is 0,indicating that the set C is not included in this combination.

As another example, entry for the combination 3 corresponds to anintersection of the sets of data B and C. This is so because the binaryrepresentation of value 3 is 011. The first position corresponding toset C and the second position corresponding to set B are both 1indicating that sets C and B are included in this combination.Furthermore, the third bit position is 0, indicating that the set A isnot included in this combination.

Following generation of the truth table, the entries of the truth tableare sorted 320 in ascending order based on the number of “trues” (orones) in the combinations corresponding to the entries. For example,FIG. 4B shows the truth table in an unsorted state (or in order of thebinary value of each entry). Illustratively, the combination 4 in thetruth table 402 is listed after the entry for the combination 3. Incontrast, the FIG. 4C, shows the truth table 402 after sorting based onthe number of “trues” (or ones) in the combinations corresponding to theentries. In particular, the entry for the combination 4 in the truthtable 402 is sorted ahead of the entry for the combination 3 because thecombination 4 includes 1 set of data (i.e., the set of data A) while thecombination 3 includes 2 sets of data (i.e., the sets of data B and C).The database engine 116 of the columnar database management system 112additionally retrieves 322 the sets of data indicated by the data miningrequest. For example, the database engine 116 may retrieve the sets ofdata A, B, and C from the data 114.

Thereafter, for each non-calculated entry in the truth table and inascending order by number of “trues,” the database engine 116 determines325 an intersection set based on those sets of data marked as true inthe combination of the non-calculated entry of the truth table. As usedherein, a non-calculated entry may refer to an entry for which theintersection set or a cardinality (number of elements) of theintersection set has not yet been determined. For example, withreference to FIG. 4C, the first non-calculated entry in ascending orderin the truth table 402 is the entry for the combination 1. Thus, thedatabase engine 116 determines the intersection set for the set C. Asshown in FIG. 4C, the determined intersection set includes all theelements of the set C because the combination 1 only includes the set C.

After determining the intersection set for the combination of thenon-calculated entry, the database engine 116 determines 330 thecardinality of the intersection set, and records the determinedcardinality as a partial result in the non-calculated entry. Referringagain to FIG. 4C, the set of data C may include 8 elements. Thus, thecardinality of the intersection set for the combination 1 is equal to 8(i.e., all the elements in the set of data C). Hence, 8 is recorded asthe “partial result” in the entry corresponding to the combination 1.

After recording the cardinality as the partial result, the databaseengine 116 marks the entry as having been calculated. The entry isadditionally marked as including the “current combination.” Thereafter,in ascending order and for each non-calculated entry including acombination higher than the current combination, the database engine 116determines 335 whether the higher combination indicated by the entryincludes at least the same sets as the current combination. For example,referring to FIG. 4D, the database engine 116 identifies the next entry,in ascending order, for a combination that includes the set C. As such,the database engine 116 identifies the entry for the combination 3.

If it is determined that the higher combination contains at least oneset that overlaps with the current combination, the database engine 116repeats steps 325, 330, and 335 for the non-calculated entry includingthe higher combination. For example, referring to FIG. 4D, the databaseengine 116 determines the intersection set between the set of data B andset of data C. In FIG. 4D, the intersection set includes 6 elements.After determining that the intersection set includes 6 elements, thedatabase engine 116 records 6 for the partial result for the combination3. The entry including the combination 3 is then set as the currentcombination. Now referring to FIG. 4E, the database engine 116identifies the next non-calculated entry for a higher combination thatincludes the sets of data B and C, which is the entry for thecombination 7. Thus, the database engine 116 determines the intersectionset between the sets of data A, B, and C. In FIG. 4E, the databaseengine 116 determines that the intersection set includes 3 elements, andrecords 3 as the partial result for the combination 7.

With continued reference to the example, since there are no remainingnon-calculated entries for combinations that include the sets of data A,B, and C, the database engine 116 processes the next non-calculatedentry in ascending order in the truth table. For example, referring toFIG. 4F, the database engine 116 identifies that the entry for thecombination 2 as not having been calculated. Thus, the database engine116 determines the intersection set for the combination 2, whichincludes all of the elements in set of data B. Hence, the databaseengine 116 records, as a partial result, 10 for the combination 2.Thereafter, the database engine 116 identifies and processes othernon-calculated entries for higher combinations including the set of dataB. Finally, the database engine 116 identifies and processes anynon-calculated entries for combinations including the set of data A.Referring now to FIG. 4G, it shows the truth table 402 with each of thepartial results for each combination recorded.

Once the intersection sets for all combinations are determined, the datamining system 108 determines the subsets of the Venn diagram based onthe intersection sets. The data mining system 108 determines a subset ofthe Venn diagram as a set difference of an intersection set and one ormore subsets of the Venn diagram that were previously computed. In anembodiment, the data mining system 108 determines a cardinality of eachsubset of the Venn diagram as a difference of a cardinality of a firstintersection set and cardinality of subsets of the Venn diagram thatwere previously computed. The data mining system 108 uses the rankingbased on the truth table to determine which subset of the Venn diagramto determine next. As described herein, the truth table ranks thecombinations of sets based on the number of input sets in eachcombination.

The data mining system 108 processes the combinations of sets indecreasing order of the number of input sets of each combination todetermine subsets of the Venn diagram. In an embodiment, the data miningsystem 108 determines a subset of the Venn diagram as the set differenceof intersection sets and previously computed subsets of Venn diagram. Inanother embodiment, the data mining system 108 determines thecardinality of each subset of the Venn diagram as the difference ofcardinality of each intersection set and cardinality of subsets of theVenn diagram that were previously computed.

Accordingly, for each entry in descending order from highest to lowest,final results are calculated 340 based on the partial results for theentry. To calculate the final result for a particular entry, thedatabase engine 116 subtracts the partial value for the particular entryby the final result for each other entry that includes (1) a highercombination than the combination of the particular entry and (2) wherethe higher combination includes the sets of data of the combination ofthe particular entry. For example, referring to FIG. 4H, it shows thetruth chart 402 following calculation of the final results for eachcombination. As shown in FIG. 4H, the final result for the entry ofcombination 3 is calculated by subtracting the partial result for theentry of combination 3 (i.e., 6) by the final result for the entry ofcombination 7 (i.e., 3). Such a calculation is performed because (1) thecombination 7 is higher than the combination 3 and (2) the combination 7includes the sets of data of the combination 3 (e.g., sets of data B andC). FIG. 4I shows the final truth table generated by the database engine116.

Thereafter, the database engine 116 generates 345 a Venn diagram basedon the final results and provides the Venn diagram to the client 105 forpresentation to a user. FIG. 4J shows an example of a Venn diagramgenerated by the database engine 116 and provided to a client 105.Although FIG. 4J shows the cardinality of various subsets of the Venndiagram, other embodiments may show other information describing thevarious subsets of the Venn diagram, for example, the elements of eachsubset or an aggregate value determined based on the elements of thesubset.

Alternative Applications

The features and advantages described in the specification are not allinclusive and, in particular, many additional features and advantageswill be apparent to one of ordinary skill in the art in view of thedrawings, specification, and claims. Moreover, it should be noted thatthe language used in the specification has been principally selected forreadability and instructional purposes, and may not have been selectedto delineate or circumscribe the inventive subject matter.

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a tangible non-transitory computer readable storagemedium or any type of media suitable for storing electronicinstructions, and coupled to a computer system bus. Furthermore, anycomputing systems referred to in the specification may include a singleprocessor or may be architectures employing multiple processor designsfor increased computing capability.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the invention is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

What is claimed is:
 1. A data mining method, comprising: receiving, by acolumnar database management system from a client device, a data miningrequest, wherein the data mining request requests generation of a Venndiagram and indicates input data sets for the Venn diagram, the columnardatabase management system having a database engine; generating, by thedatabase engine, a truth table based on the input data sets for the Venndiagram, the truth table having multiple entries, where each entry ofthe multiple entries corresponds to a combination of the input data setsand is associated with a binary value, wherein positions of bits in thebinary value correspond to the input data sets, wherein a value of a bitdetermines whether an input data set, which corresponds to a position ofthe bit, is included in the combination represented by the binary value;ranking, by a data mining system, combinations of the input data setsbased on a number of ones in each binary value associated with each ofthe combinations of the input data sets; and selecting, by the datamining system utilizing the ranking, a next interaction set to processfor the Venn diagram after a particular intersection set is processed tothereby determine intersection sets for the Venn diagram in an orderthat efficiently utilizes the particular intersection set that has beenpreviously processed.
 2. The method according to claim 1, furthercomprising: determining the value of the bit, wherein, responsive to thevalue of the bit being determined as zero, the input data set, whichcorresponds to the position of the bit, is not included in thecombination represented by the binary value.
 3. The method according toclaim 1, wherein the data mining request is received by the columnardatabase management system directly from the client device or via afrontend application of the data mining system.
 4. The method accordingto claim 1, wherein the columnar database management system is part ofthe data mining system.
 5. The method according to claim 1, wherein thedata mining request is encapsulated in a text format.
 6. The methodaccording to claim 1, further comprising: storing the intersection setsprocessed by the data mining system for the Venn diagram.
 7. The methodaccording to claim 1, further comprising: communicating the intersectionsets processed by the data mining system for the Venn diagram to theclient device.
 8. A data mining system, comprising: a columnar databasemanagement system having: a processor; a non-transitorycomputer-readable medium; and stored instructions translatable by theprocessor for: receiving, from a client device, a data mining request,wherein the data mining request requests generation of a Venn diagramand indicates input data sets for the Venn diagram; generating a truthtable based on the input data sets for the Venn diagram, the truth tablehaving multiple entries, where each entry of the multiple entriescorresponds to a combination of the input data sets and is associatedwith a binary value, wherein positions of bits in the binary valuecorrespond to the input data sets, wherein a value of a bit determineswhether an input data set, which corresponds to a position of the bit,is included in the combination represented by the binary value; rankingcombinations of the input data sets based on a number of ones in eachbinary value associated with each of the combinations of the input datasets; and selecting, utilizing the ranking, a next interaction set toprocess for the Venn diagram after a particular intersection set isprocessed to thereby determine intersection sets for the Venn diagram inan order that efficiently utilizes the particular intersection set thathas been previously processed.
 9. The data mining system of claim 8,wherein the stored instructions are further translatable by theprocessor for: determining the value of the bit, wherein, responsive tothe value of the bit being determined as zero, the input data set, whichcorresponds to the position of the bit, is not included in thecombination represented by the binary value.
 10. The data mining systemof claim 8, further comprising: a frontend application, wherein the datamining request is received by the columnar database management systemdirectly from the client device or via the frontend application of thedata mining system.
 11. The data mining system of claim 8, wherein thedata mining request is encapsulated in a text format.
 12. The datamining system of claim 8, wherein the stored instructions are furthertranslatable by the processor for: storing the intersection setsprocessed by the data mining system for the Venn diagram.
 13. The datamining system of claim 8, wherein the stored instructions are furthertranslatable by the processor for: communicating the intersection setsprocessed by the data mining system for the Venn diagram to the clientdevice.
 14. A computer program product for data mining, the computerprogram product comprising a non-transitory computer-readable mediumstoring instructions translatable by a processor of a columnar databasemanagement system for: receiving, from a client device, a data miningrequest, wherein the data mining request requests generation of a Venndiagram and indicates input data sets for the Venn diagram; generating atruth table based on the input data sets for the Venn diagram, the truthtable having multiple entries, where each entry of the multiple entriescorresponds to a combination of the input data sets and is associatedwith a binary value, wherein positions of bits in the binary valuecorrespond to the input data sets, wherein a value of a bit determineswhether an input data set, which corresponds to a position of the bit,is included in the combination represented by the binary value; rankingcombinations of the input data sets based on a number of ones in eachbinary value associated with each of the combinations of the input datasets; and selecting, utilizing the ranking, a next interaction set toprocess for the Venn diagram after a particular intersection set isprocessed to thereby determine intersection sets for the Venn diagram inan order that efficiently utilizes the particular intersection set thathas been previously processed.
 15. The computer program product of claim14, wherein the instructions are further translatable by the processorfor: determining the value of the bit, wherein, responsive to the valueof the bit being determined as zero, the input data set, whichcorresponds to the position of the bit, is not included in thecombination represented by the binary value.
 16. The computer programproduct of claim 14, wherein the data mining request is received by thecolumnar database management system directly from the client device orvia a frontend application of a data mining system.
 17. The computerprogram product of claim 15, wherein the columnar database managementsystem is part of the data mining system.
 18. The computer programproduct of claim 14, wherein the data mining request is encapsulated ina text format.
 19. The computer program product of claim 14, wherein theinstructions are further translatable by the processor for: storing theintersection sets for the Venn diagram.
 20. The computer program productof claim 14, wherein the instructions are further translatable by theprocessor for: communicating the intersection sets for the Venn diagramto the client device.